This is an example of a whole range of beliefs about LLMs that are very common (even in the field itself), because they were obviously true for small models, but that might not necessarily hold for larger models. There's a lot that we don't know about LLMs, but we do know that they exhibit emergent behaviors as they scale. Smaller models don't really have world models, just language models, but these larger models have started developing clear world models once given the capacity and data to do so.
As for the existence of a concept of factuality, I found this paper[1] very interesting. It details an unsupervised method to identify which internal activations of the model correspond to factual statements, regardless of what the model ends up saying. Looking at those internal activations rather than just the model's output even reduces the model's susceptibility to prompts that lead it towards saying the wrong answer.